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Session 7, Track A: Optimizing Canadian Submissions: Risk-Based Framework Approach for Package Inserts, XML PM Authoring and CMC Trends
Session Chair(s)
Adesola Adeyemi, MSc
Regulatory Affairs Project Manager, Bayer Inc., Canada
This session will focus on advancing regulatory decision-making through integrated, structured approaches to labeling, XML PM authoring, and CMC submission quality. It will explore how a risk-based framework and risk assessment tool is improving consistency and transparency in package insert decisions across directorates, alongside practical strategies to implement compliant, efficient XML Product Monograph authoring workflows under evolving Health Canada requirements. Participants will also gain insights into how structured CMC authoring reduces variability, mitigates supply risk, and supports more robust regulatory outcomes in an increasingly complex landscape. Attendees will leave with actionable frameworks, practical strategies, and a clearer understanding of how to strengthen submission quality, streamline processes, and reduce regulatory delays through structured, risk-based approaches.
Learning Objective : - Understand Health Canada’s structured, risk-based framework guiding recommendations on package insert inclusion or omission
- Understand the foundational steps for XML PM authoring, identify common challenges and apply practical strategies and tools to streamline XML PM creation, validation and publishing
- Identify and address CMC submission quality gaps that drive delays and supply risk and recognize current and emerging CMC regulatory trends
Speaker(s)
Amanda Starr, PhD
Scientific Evaluator - Pharmaceutical Drugs Directorate, Health Canada, Canada
Advancing a Risk-Based Framework for Package Insert Decision-Making Across Directorates
Kenneth Gutierrez
Senior Product Manager, Docuvera, United States
CMC Submission Quality as Supply Risk: How Structured Authoring Reduces Variability and Regulatory Delay
Melissa Bieman, PhD
Manager, Regulatory Affairs, Intrinsik Corp., Canada
XML PM Authoring: Practical Strategies, Challenges, and Insights